A general approach for multivariate statistical MOSFET compact modeling preserving correlations

Lange, André; Sohrmann, Christoph; Jancke, Roland; Haase, Joachim; Cheng, Binjie; Kovac, Urban; Asenov, A.

doi:10.1109/essderc.2011.6044209

Cited by 12 publications

(8 citation statements)

References 13 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of generating data from a multivariate generalized lambda distribution, it is a bit more complicated. In the paper, we use the procedure of generating data from a multivariate non-normal distribution given in [4]. The application of this procedure requires that the distributions of the individual components of the random variable 𝑋 must be known in the form of quantile functions and the correlation is available as a correlation matrix 𝑅 𝑋 using rank-based correlation (e.g.…”

Section: Resampling Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Application of Multivariate Lambda Distribution Within the Portfolio Selection Model

Pčolár¹

2022

EDAMBA 2021 : COVID-19 Recovery: The Need for Speed : Conference Proceedings

View full text Add to dashboard Cite

The article deals with the application of the resampling procedure using multivariate lambda distribution within the process of optimization of portfolio selection models. The aim of the resampling procedure is to achieve portfolios that provide better quality results on out-of-sample data compared to the traditional optimization-based approach using estimates from historical data. In this paper, we deal with the application of the resampling procedure on daily data of 30 assets within the model of portfolio selection in the space of expected return and CVaR (Conditional Value at Risk). We are dealing with the application of two approaches, an approach based on the assumption of normal distribution of data using multivariate normal distribution for data generation and a procedure using data generation from multivariate generalized lambda distribution.

show abstract

Section: Resampling Proceduresmentioning

confidence: 99%

“…Spearman's correlation coefficient). Subsequently, the procedure consists of the following steps [4] The advantage of such procedure is considerable flexibility in the choice of the assumed distribution to the point that the quantile function must be known for the selected distribution.…”

Section: Resampling Proceduresmentioning

confidence: 99%

Application of Multivariate Lambda Distribution Within the Portfolio Selection Model

Pčolár¹

2022

EDAMBA 2021 : COVID-19 Recovery: The Need for Speed : Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…We consider them as 7-dimensional RVs X N and X P for the NFET and the PFET. The device simulations generate samples X N and X P of the size N char = 200 as the basis for deriving the probabilistic models [51].…”

Section: A Probabilistic Mosfet Compact Modeling and Statistical Circ...mentioning

confidence: 99%

Multivariate Modeling of Variability Supporting Non-Gaussian and Correlated Parameters

Lange

Sohrmann

Jancke

et al. 2016

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Self Cite

View full text Add to dashboard Cite

Process variations and atomic-level fluctuations increasingly pose challenges to the design and analysis of integrated circuits by introducing variability. Although several approaches have been proposed to deal with the inherent statistical nature of circuit design, we consider them incomplete with two important aspects often being insufficiently addressed: non-Gaussian distributions and highly correlated parameters. To address these points, we propose a fully multivariate and non-Gaussian approach based on an arbitrary model. A subset of the model parameters is treated as a multi-dimensional random variable, which is represented by a combination of generalized lambda distributions and Spearman rank correlation matrices -a very general approach with nearly arbitrary freedom in distribution shapes and parameter correlations. In our application scenarios, we show that such a model is able to fully and accurately capture variability in device compact models and standard cell performance models. Finally, we present adapted analysis methods making use of these models in circuit simulations and in efficient gate level analyses of digital circuits with high accuracy. I. INTRODUCTIONP ROCESS VARIATIONS and atomic-level fluctuations result in integrated circuit (IC) performance variability. To achieve circuits fulfilling their specifications, such effects have to be taken into account during circuit design and analysis [1].Global variations shift the parameters of all devices per die equally. They have always affected ICs and can be tackled by corner-based analysis approaches that assume the most extreme parameter combinations [2]. However, local variations, causing identically designed devices on the same die and in close proximity to randomly differ, have rapidly gained importance since the 90 nm technology node [2]. In combination, global and local variations lead to correlated device variability. Neglecting local variations, corner-based approaches are not sufficient any more. Instead, new modeling and analysis methods are required to adequately analyze the impact of variability during IC design [2]-[4].In Sec. II, the state of the art to handle variability is reviewed for device compact models and digital circuit design. This survey shows that a variety of methods already exists, but most of them focus on particular tasks and often make A. Lange, Ch. Sohrmann, R. Jancke, and J. Haase are with the Fraunhofer Institute for Integrated Circuits (IIS), Design Automation Division (EAS), 01069 Dresden, Germany (email: [andre.lange, christoph.sohrmann, roland.janke, joachim.haase]@eas.iis.fraunhofer.de).

show abstract

“…Previous studies of FinFET variability based on the statistical TCAD have focused on the global and/or mismatch variability for either nFET or pFET. However, most studies consider the cross correlation of device parameters within nFETs or pFETs and the process enhancement to mitigate variability [14]- [18], yet there is no comprehensive study on n-to-p tracking characteristics of FinFET. In this paper, statistical TCAD simulations considering nFET and pFET systematic global process variation and correlations are implemented to provide electrical n-to-p tracking characteristic targets in the early development stage of 14-nm SOI FinFET technology and to reduce the overall design cycle time.…”

Section: Introductionmentioning

confidence: 99%

SOI FinFET nFET-to-pFET Tracking Variability Compact Modeling and Impact on Latch Timing

Deng

Rahman

Thoma

et al. 2015

IEEE Trans. Electron Devices

View full text Add to dashboard Cite

In this paper, nFET-to-pFET (n-to-p) tracking characteristics in 14-nm silicon-on-insulator (SOI) FinFET technology are studied by technology computer-aided design-based statistical modeling. Compared with planar SOI high-k metal gate CMOS technologies, 14-nm SOI FinFET technology shows better n-to-p tracking mainly due to the strong influence of correlated Fin geometrical variation, as well as reduced uncorrelated variation from an innovative work function process. The impact of the n-to-p tracking characteristics on setup and hold (guard time) of latch circuits is evaluated by corner and Monte Carlo simulation using compact models. It is found that the guard time is significantly modulated by slow/fast and fast/slow corners in certain conditions and, therefore should be considered in guard time design.Index Terms-Integrated circuits design, MOSFETs, semiconductor device modeling, variation aware timing. 0018-9383

show abstract

A general approach for multivariate statistical MOSFET compact modeling preserving correlations

Cited by 12 publications

References 13 publications

Application of Multivariate Lambda Distribution Within the Portfolio Selection Model

Application of Multivariate Lambda Distribution Within the Portfolio Selection Model

Multivariate Modeling of Variability Supporting Non-Gaussian and Correlated Parameters

SOI FinFET nFET-to-pFET Tracking Variability Compact Modeling and Impact on Latch Timing

Contact Info

Product

Resources

About